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Effective groundwater management is critical to future environmental, ecological, and social sustainability and requires accurate estimates of groundwater withdrawals. Unfortunately, these estimates are not readily available in most areas due to physical, regulatory, and social challenges. Here, we compare four different approaches for estimating groundwater withdrawals for agricultural irrigation. We apply these methods in a groundwater‐irrigated region in the state of Kansas, USA, where high‐quality groundwater withdrawal data are available for evaluation. The four methods represent a broad spectrum of approaches: (1) the hydrologically‐based Water Table Fluctuation method (WTFM); (2) the demand‐based SALUS crop model; (3) estimates based on satellite‐derived evapotranspiration (ET) data from OpenET; and (4) a landscape hydrology model which integrates hydrologic‐ and demand‐based approaches. The applicability of each approach varies based on data availability, spatial and temporal resolution, and accuracy of predictions. In general, our results indicate that all approaches reasonably estimate groundwater withdrawals in our region, however, the type and amount of data required for accurate estimates and the computational requirements vary among approaches. For example, WTFM requires accurate groundwater levels, specific yield, and recharge data, whereas the SALUS crop model requires adequate information about crop type, land use, and weather. This variability highlights the difficulty in identifying what data, and how much, are necessary for a reasonable groundwater withdrawal estimate, and suggests that data availability should drive the choice of approach. Overall, our findings will help practitioners evaluate the strengths and weaknesses of different approaches and select the appropriate approach for their application. 

The condition of the Salton Sea, California's largest lake, has profound implications for people and wildlife both near and far. Colorado River irrigation water has supported agricultural productivity in the basin's Coachella and Imperial valleys since the Sea formed over 100 years ago, bringing billions of dollars per year to the region and helping to feed households across the United States. The runoff, which drains into the Sea, has historically maintained water levels and supported critical fish and migratory bird habitats. However, since 2018, a large portion of the water previously allocated for agriculture has been diverted to urban regions, causing the Sea to shrink and become increasingly saline. This poses major threats to the Sea's ecology, as well as risks to human health, most notably in the noxious dust produced by the drying lakebed. To ensure continued agricultural and ecological productivity and protect public health, management of the Sea and surrounding wetlands will require increased research and mitigation efforts. 

Abstract. Mountainous regions act as the water towers of the worldby producing streamflow and groundwater recharge, a function that isparticularly important in semiarid regions. Quantifying rates of mountainsystem recharge is difficult, and hydrologic models offer a method toestimate recharge over large scales. These recharge estimates are prone touncertainty from various sources including model structure and parameters.The quality of meteorological forcing datasets, particularly in mountainousregions, is a large source of uncertainty that is often neglected ingroundwater investigations. In this contribution, we quantify the impact ofuncertainty in both precipitation and air temperature forcing datasets onthe simulated groundwater recharge in the mountainous watershed of theKaweah River in California, USA. We make use of the integrated surface water–groundwater model, ParFlow.CLM, and several gridded datasets commonly usedin hydrologic studies, downscaled NLDAS-2, PRISM, Daymet, Gridmet, andTopoWx. Simulations indicate that, across all forcing datasets, mountain front recharge is an important component of the water budget in themountainous watershed, accounting for 9 %–72 % of the annual precipitation and ∼90 % of the total mountain system recharge to theadjacent Central Valley aquifer. The uncertainty in gridded air temperatureor precipitation datasets, when assessed individually, results in similarranges of uncertainty in the simulated water budget. Variations in simulatedrecharge to changes in precipitation (elasticities) and air temperature(sensitivities) are larger than 1 % change in recharge per 1 % change inprecipitation or 1 ∘C change in temperature. The total volume ofsnowmelt is the primary factor creating the high water budget sensitivity, and snowmelt volume is influenced by both precipitation and air temperatureforcings. The combined effect of uncertainty in air temperature andprecipitation on recharge is additive and results in uncertainty levels roughly equal to the sum of the individual uncertainties depending on thehydroclimatic condition of the watershed. Mountain system recharge pathwaysincluding mountain block recharge, mountain aquifer recharge, and mountainfront recharge are less sensitive to changes in air temperature than changesin precipitation. Mountain front and mountain block recharge are moresensitive to changes in precipitation than other recharge pathways. Themagnitude of uncertainty in the simulated water budget reflects theimportance of developing high-quality meteorological forcing datasets in mountainous regions. 

The fragile balance of endorheic lakes in highly managed semiarid basins with transboundary water issues has been altered by the intertwined effects of global warming and long‐term water mismanagement to support agricultural and industrial demand. The alarming rate of global endorheic lakes' depletion in recent decades necessitates formulating mitigation strategies for ecosystem restoration. However, detecting and quantifying the relative contribution of causal factors (climate variability and anthropogenic stressors) is challenging. This study developed a diagnostic multivariate framework to identify major hydrologic drivers of lake depletion in a highly managed endorheic basin with a complex water distribution system. The framework integrates the Soil and Water Assessment Tool (SWAT) simulations with time series decomposition and clustering methods to identify the major drivers of change. This diagnostic framework was applied to the Salton Sea Transboundary Basin (SSTB), the host of the world's most impaired inland lake. The results showed signs of depletion across the SSTB since late 1998 with no significant changes in climate conditions. The time series data mining of the SSTB water balance components indicated that decreases in lake tributary inflows (−16.4 Mm³yr⁻²) in response to decline in Colorado River inflows, associated with state water transfer agreements, are causing the Salton Sea to shrink, not changes in the irrigation operation as commonly believed. The developed multivariate detection and attribution framework is useful for identifying major drivers of change in coupled natural human systems.

 

Soil biota generate CO2 that can vertically export to the atmosphere, and dissolved organic and inorganic carbon (DOC and DIC) that can laterally export to streams and accelerate weathering. These processes are regulated by external hydroclimate forcing and internal structures (permeability distribution), the relative influences of which are rarely studied. Understanding these interactions is essential a hydrological extremes intensify in the future. Here we explore the question: How and to what extent do hydrological and permeability distribution conditions regulate soil carbon transformations and chemical weathering? We address the questions using a hillslope reactive transport model constrained by data from the Fitch Forest (Kansas, United States). Numerical experiments were used to mimic hydrological extremes and variable shallow-versus-deep permeability contrasts. Results demonstrate that under dry conditions (0.08 mm/day), long water transit times led to more mineralization of organic carbon (OC) into inorganic carbon (IC) form (>98\%). Of the IC produced, ~ 75\% was emitted upward as CO2 gas and ~ 25\% was exported laterally as DIC into the stream. Wet conditions (8.0 mm/day) resulted in less mineralization (~88\%), more DOC production (~12\%), and more lateral fluxes of IC (~50\% of produced IC). Carbonate precipitated under dry conditions and dissolved under wet conditions as the fast flow rapidly droves the reaction to disequilibrium. The results depict a conceptual hillslope model that prompts four hypotheses for our community to test. H1: Droughts enhance carbon mineralization and vertical upward carbon fluxes, whereas large hydrological events such as storms and flooding enhance subsurface vertical connectivity, reduce transit times, and promote lateral export. H2: The role of weathering as a net carbon sink or source to the atmosphere depends on the interaction between hydrologic flows and lithology: transition from droughts to storms can shift carbonate from a carbon sink (mineral precipitation) to carbon source (dissolution). H3: Permeability contrasts regulate the lateral flow partitioning via shallow flow paths versus deeper groundwater though this alter reaction rates negligibly. H4: Stream chemistry reflect flow paths and can potentially quantify water transit times: solutes enriched in shallow soils have a younger water signature; solutes abundant at depth carry older water signature. 

Understanding soil organic carbon (SOC) response to global change has been hindered by an inability to map SOC at horizon scales relevant to coupled hydrologic and biogeochemical processes. Standard SOC measurements rely on homogenized samples taken from distinct depth intervals. Such sampling prevents an examination of fine‐scale SOC distribution within a soil horizon. Visible near‐infrared hyperspectral imaging (HSI) has been applied to intact monoliths and split cores surfaces to overcome this limitation. However, the roughness of these surfaces can influence HSI spectra by scattering reflected light in different directions posing challenges to fine‐scale SOC mapping. Here, we examine the influence of prescribed surface orientation on reflected spectra, develop a method for correcting topographic effects, and calibrate a partial least squares regression (PLSR) model for SOC prediction. Two empirical models that account for surface slope, aspect, and wavelength and two theoretical models that account for the geometry of the spectrometer were compared using 681 homogenized soil samples from across the United States that were packed into sample wells and presented to the spectrometer at 91 orientations. The empirical approach outperformed the more complex geometric models in correcting spectra taken at non‐flat configurations. Topographically corrected spectra reduced bias and error in SOC predicted by PLSR, particularly at slope angles greater than 30°. Our approach clears the way for investigating the spatial distributions of multiple soil properties on rough intact soil samples.

 

Urban areas are the primary source of human-made litter globally, and roadsides are a primary accumulation location. This study aimed to investigate how litter arrives at roadsides and determine the accumulation rate and composition of roadside litter. We monitored select roadsides in the Inland Empire, California, for litter abundance (count) and composition (material, item, and brand type). Receipt litter with sale time and location information was used to investigate whether wind, runoff, or human travel were dominant transport agents. Only 9% of the receipts could have experienced runoff, and wind direction was not correlated with receipt transport direction. However, human travel and receipt transport distances were similar in magnitude and distribution, suggesting that the displacement of litter from the place of purchase was predominantly affected by human travel. The median distance receipts traveled from the sale location to the litter observation location was 1.6 km, suggesting that most sources were nearby to where the litter was found. Litter accumulation rates were surprisingly stable (mean 40 349 (33 255–47 865) # km⁻¹yr⁻¹or 1170 (917–1447) kg km⁻¹yr⁻¹) despite repeated cleanups and the COVID-19 stay-at-home orders. A new approach was employed to hierarchically bootstrap litter composition proportions and estimate uncertainties. The most abundant materials were plastic and paper. Food-related items and tobacco products were the most common item types. The identified branded objects were from the primary manufacturers (Philip Morris (4, 2%–7%), Mars Incorporated (2, 1%–3%), RJ Reynolds (2, 1%–3%), and Jack in The Box (1, 1%–3%)), but unbranded objects were prevalent. Therefore, identifiable persistent labeling on all products would benefit future litter-related corporate social responsibility efforts. High-resolution monitoring on roadsides can inform urban litter prevention strategies by elucidating litter source, transport, and accumulation dynamics.